Independent learning (IL) dates back to the early days of multi-agent reinforcement learning [1]}. While initially tabular, IL algorithms using neural networks as function approximators were subsequently developed [IAC, deep IQL][2]}, [3]}.
The question of whether independent agents are better at learning cooperative behaviour than multiple complete observing agents [4]} was first comprehensively investigated by Tan [1]}, who concludes that while sharing policies or experience was generally advantageous, sharing extra sensory information could also negatively interfere with learning, e.g., by expanding the state space.
